This year has seen a wave of mass layoffs sweeping through major corporations, with Snapchat's parent company cutting 16 percent of its workforce, Block Inc. slashing 40 percent, and Meta recently trimming 10 percent. The trend has even led the Wall Street Journal to question whether the U.S. has entered an era of “mega-layoffs.”
These cuts are often touted to investors as a strategic shift: expensive human workers are being replaced by cheaper artificial intelligence, freeing up capital for further tech investments. But while companies boast about innovation, they remain conspicuously silent on which specific jobs are being eliminated.
Lisa LaViers, an assistant professor of accounting at Tulane’s A.B. Freeman School of Business, argues that this silence is a critical problem. In a recent analysis, she contends that if AI is fundamentally reshaping the labor market, the public deserves to know—in real time—which roles are vanishing. “Firms should be required to publicly disclose which jobs are being eliminated due to AI as soon as it happens,” she writes.
The broader debate over AI’s impact on society is intensifying. In Silicon Valley, fears of mass white-collar firings have sparked discussions about universal basic income. Shedding millions of jobs could unleash economic disparity and labor market distress on an unprecedented scale. LaViers warns that we are at the beginning of this potential transition, but still have time to act.
Transparency, she argues, is key to a smoother landing. Accurate, timely data on which jobs are being displaced would allow workers to retrain for in-demand skills, guide universities in steering students toward viable majors, and help policymakers spot emerging disruptions before they spiral into crises.
Currently, U.S. firms are legally required to disclose the scale and timing of layoffs through WARN notices and, for public companies, 8-K filings with the Securities and Exchange Commission. But these filings are procedural—they announce headcounts, severance dates, and site locations, or emphasize restructuring charges and cash impacts. They do not specify which types of employees are cut, and companies rarely volunteer that information. In an era where entire job categories could be wiped out by AI, LaViers says these barebones disclosures are insufficient.
She proposes a practical, low-cost fix: require firms to include standardized occupation codes and brief descriptions of affected jobs in WARN notices. Additionally, firms should disclose the demographics—by gender, race, and age—of those laid off, so policymakers can assess whether certain groups are disproportionately impacted. Critics may argue that such disclosures are too costly, but LaViers counters that the benefits of a healthier labor market outweigh compliance expenses. Unlike other regulations that demand new data creation, firms already classify employees for EEOC, Census, and internal HR purposes; they need only share that information publicly.
This push for transparency aligns with broader calls for accountability in the tech sector. For instance, the UN chief recently demanded that AI firms disclose their environmental costs and commit to clean energy by 2030. Similarly, LaViers’ proposal seeks to shed light on the human costs of automation. “As a society, we need to work to maximize innovation in AI while also smoothing the transition for affected workers,” she writes.
Without richer layoff notices, scholars, employees, and policymakers are left acting on fears and hypotheticals. If the U.S. were to mandate more detailed disclosures, it could craft common-sense policies grounded in real-time economic data, helping the nation navigate the AI-driven transformation of work.
